Libraries tagged by heap analytics

Paragon Processing PlatformThis document is to provide a detailed description of how a developer or software solution provider can integrate with the Paragon Processing Platform (Paragon Platform). The Paragon Platform is for developers who wish to use Paragon's processing services within their software applications. The Paragon Platform can stand alone as a web-based storefront with (or without) attached card readers or may be used in combination with point-of-sale (POS) and order entry applications.The Paragon Platform accepts payments in many forms, including check, credit, debit, gift, loyalty and EBT cards. Payments may be taken online, over the telephone, from a mobile phone and at physical store locations. In addition to handling large volumes of payment transactions, the Paragon Platform manages and settles batches, protects sensitive card information, administers billing contracts and produces operational and analytical reports. Application Programming Interfaces (APIs) With our API, software systems and devices communicate with the Paragon Platform by exchanging messages. Client Application formulates a message and transmits the request to the Paragon Platform. The Paragon Platform translates the request and relays it to the Payment Processing Network. The Payment Processing Network processes the request and returns a response to the Paragon Platform. The Paragon Platform translates the response and relays it to the Client Application.The Path to Integration Step 1: Open a Test Account Visit https://developer.paragonsolutions.com/testaccount to register for a free test account. Once registered you have instant access to your test account credentials and all the documentation you need to get started. Head over to the library and make sure to check out all of our API's to learn about the Paragon Processing Platform transaction requests and responses.Step 2: Code & CertifyOnce in the Paragon Development portal, begin your certification or have an experienced Paragon integration specialist guide you through the testing process. Once coding and testing are completed submit your request for certification. Our team will then perform a detailed review of your integration to make sure your integration meets all your business requirements for success. Step 3: Go Live!After final review and testing your software solution is ready to begin taking live payments! We know, an integration that is easy sounds too good to be true. Don't take our word for it, start your integration today! Developer NotesInstall your development platform according to its product documentation.Read through all the steps before you get started. We recommend reviewing the rest of this guide to familiarize yourself with the Paragon Processing Platform's terms, capabilities, web services and operations.

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A set of rules for the Psalm static analysis tool that help to detect popular issues when working with the Laravel framework.

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Free and open source server providing code assistance for PHP

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AutoIt Obfuscator Web API interface can help you to protect your AutoIt script source code against analysis, reverse engineering and decompilation. AutoIt Obfuscator provides advanced AutoIt source code parsing based on AST trees, multiple advanced obfuscation strategies are available.

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# Introduction **INDA (INtelligent Data Analysis)** is an [Intervieweb](https://www.intervieweb.it/hrm/) AI solution provided as a RESTful API. The INDA pricing model is *credits-based*, which means that a certain number of credits is associated to each API request. Hence, users have to purchase a certain amount of credits (established according to their needs) which will be reduced at each API call. INDA accepts and processes a user's request only if their credits quota is grater than - or, at least, equal to - the number of credits required by that request. To obtain further details on the pricing, please visit our [site](https://inda.ai) or contact us. INDA HR embraces a wide range of functionalities to manage the main elements of a recruitment process: + [**candidate**](https://api.inda.ai/hr/docs/v2/#tag/Resume-Management) (hereafter also referred to as **resume** or **applicant**), or rather a person looking for a job; + [**job advertisement**](https://api.inda.ai/hr/docs/v2/#tag/JobAd-Management) (hereafter also referred to as **job ad**), which is a document that collects all the main information and details about a job vacancy; + [**application**](https://api.inda.ai/hr/docs/v2/#tag/Application-Management), that binds candidates to job ads; it is generated whenever a candidate applies for a job. Each of them has a specific set of methods that grants users the ability to create, read, update and delete the relative documents, plus some special features based on AI approaches (such as *document parsing* or *semantic search*). They can be explored in their respective sections. Data about the listed document types can be enriched by connecting them to other INDA supported entities, such as [**companies**](https://api.inda.ai/hr/docs/v2/#tag/Company-Management) and [**universities**](https://api.inda.ai/hr/docs/v2/#tag/Universities), so that recruiters may get a better and more detailed idea on the candidates' experiences and acquired skills. All the functionalities mentioned above are meant to help recruiters during the talent acquisition process, by exploiting the power of AI systems. Among the advantages a recruiter has by using this kind of systems, tackling the bias problem is surely one of the most relevant. Bias in recruitment is a serious issue that affect both recruiters and candidates, since it may cause wrong hiring decisions. As we care a lot about this problem, we are constantly working on reduce the bias in original data so that INDA results may be as fair as possible. As of now, in order to tackle the bias issue, INDA automatically ignores specific fields (such as name, gender, age and nationality) during the initial processing of each candidate data. Furthermore, we decided to let users collect data of various types, including personal or sensitive details, but we do not allow their usage if it is different from statistical purposes; our aim is to discourage recruiters from focusing on candidates' personal information, and to put their attention on the candidate's skills and abilities. We want to help recruiters to prevent any kind of bias while searching for the most valuable candidates they really need. The following documentation is addressed both to developers, in order to provide all technical details for INDA integration, and to managers, to guide them in the exploration of the implementation possibilities. The host of the API is [https://api.inda.ai/hr/v2/](https://api.inda.ai/hr/v2/). We recommend to check the API version and build (displayed near the documentation title). You can contact us at [email protected] in case of problems, suggestions, or particular needs. The search panel on the left can be used to navigate through the documentation and provides an overview of the API structure. On the right, you can find (*i*) the url of the method, (*ii*) an example of request body (if present), and (*iii*) an example of response for each response code. Finally, in the central section of each API method, you can find (*i*) a general description of the purpose of the method, (*ii*) details on parameters and request body schema (if present), and (*iii*) details on response schema, error models, and error codes.

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Free and open source server providing code assistance for PHP

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Analysis of the useragent in the request head

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Simple collections of make commands to increase productivity. This package gives you make commands to run your project easily. You can also customize the make commands to fit your project needs. Make commands o help work with docker, git, composer, migrations symfony, migrations laravel, tests, static analysis, and more.

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A PHP library that help you calculates the Eigenvector Centrality value of an undirected graph. Eigenvector Centrality is one of the Social Network Analysis algorithms.

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Search Package OverviewThe Search package is a powerful tool designed to facilitate efficient and effective search operations within various datasets or databases. It provides a set of functions and classes that enable users to perform complex search queries, filter results, and retrieve relevant data with ease. The package is highly customizable, allowing users to define their own search criteria, implement sorting mechanisms, and handle large volumes of data seamlessly.Key Features: Customizable Search Queries: Users can create tailored search queries using various operators and conditions, making it possible to perform both simple and advanced searches. Sorting and Filtering: The package includes built-in methods to sort and filter search results, enabling users to organize data based on specific parameters such as date, relevance, or custom fields. Scalability: Designed to handle large datasets, the Search package is optimized for performance, ensuring quick response times even with millions of records. Integration: The package is compatible with a variety of databases and data sources, making it a versatile solution for different types of projects. User-Friendly Interface: It offers a straightforward API that is easy to use, even for those who are not experts in programming. This allows a broader audience to leverage the power of advanced search capabilities.Use Cases: Data Analysis: Quickly find and retrieve specific information from large datasets for analysis. Content Management Systems: Implement efficient search functionality in content-heavy websites or applications. E-commerce: Enhance product search features in online stores, improving the user experience by providing relevant results swiftly. Knowledge Bases: Help users find relevant articles or documentation based on keyword searches.Overall, the Search package is an essential tool for anyone needing to implement or enhance search functionality in their applications, providing both power and flexibility in managing and retrieving data.

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